Improving AI Speech Recognition with External AM Integration
Recent advancements in artificial intelligence (AI) have led to significant improvements in automatic speech recognition (ASR). The integration of an external acoustic model (AM) into the end-to-end (E2E) ASR system has shown promising results in addressing domain mismatch issues and enhancing named entity recognition.
The Significance of External AM Integration
The E2E ASR system, which combines the acoustic model and the language model into a single network, has revolutionized speech recognition technology. However, the application of traditional language model fusion has limitations in addressing domain mismatch. By integrating an external AM, the E2E system can better adapt to different domains, leading to improved accuracy and reduced word error rate.
Benefits of AM Fusion Approach
Through our research, we have observed a significant drop of up to 14.3% in word error rate across various test sets. Additionally, the integration of an external AM has proven to be particularly effective in enhancing named entity recognition, further demonstrating the potential of this innovative approach in AI speech recognition technology.